library(swimplot) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(rms)

#ctDNA positivity by stage and window

#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA C5D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C5D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C5D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA C8D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C8D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C8D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA EOT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.EOT!="",]
circ_data$ctDNA.EOT <- factor(circ_data$ctDNA.EOT, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.EOT %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.EOT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.EOT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.EOT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA anytime
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Heatmap for clinicopathologic factors

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Sex = circ_data$Sex,
  PrimSite = circ_data$PrimSite,
  pT = circ_data$pT,
  Pathology = circ_data$Pathology,
  Chemo = circ_data$Chemo,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.C5D1 = circ_data$ctDNA.C5D1,
  ctDNA.C8D1 = circ_data$ctDNA.C8D1,
  ctDNA.EOT = circ_data$ctDNA.EOT,
  ctDNA.anytime = circ_data$ctDNA.anytime,
  RecStatus = circ_data$RecStatus,
  VitalStatus = circ_data$VitalStatus,
  
    col = list(Stage = c("II" = "seagreen2", "III" = "orange", "IV" = "purple"),
    Sex = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("pCCA" = "darkgreen", "dCCA" ="#008BCE"),
    pT = c("T1" = "lightblue", "T2" ="orange", "T3" = "brown" ),
    Pathology = c("G1" = "yellow3", "G2" ="darkgreen", "G3" = "brown2"),
    Chemo = c("CAP" = "lightblue", "GemCis" = "orange2"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C5D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C8D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.EOT = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    RFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
    OS.Event = c("TRUE" = "black", "FALSE" ="grey")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
null device 
          1 

#Prognostic role of ctDNA at the MRD time point

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 67     45  15.84   11.96    27.6
ctDNA.MRD=POSITIVE 22     20   8.23    7.86    15.5
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA MRD timepoint", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     38      28    0.577  0.0607        0.450        0.686
   24     26      11    0.407  0.0608        0.288        0.523

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7      15    0.318  0.0993       0.1418        0.511
   24      5       2    0.227  0.0893       0.0827        0.414
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 89, number of events= 65 

                    coef exp(coef) se(coef)     z Pr(>|z|)  
ctDNA.MRDPOSITIVE 0.5902    1.8043   0.2705 2.182   0.0291 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     1.804     0.5542     1.062     3.066

Concordance= 0.556  (se = 0.028 )
Likelihood ratio test= 4.38  on 1 df,   p=0.04
Wald test            = 4.76  on 1 df,   p=0.03
Score (logrank) test = 4.9  on 1 df,   p=0.03
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.8 (1.06-3.07); p = 0.029"

#OS by ctDNA at the MRD time point

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 67     39   38.5    28.9      NA
ctDNA.MRD=POSITIVE 22     17   30.8    22.6      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MRD timepoint", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     65       2    0.970  0.0208        0.886        0.992
   24     50      15    0.746  0.0532        0.624        0.834

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     17       5    0.773  0.0893        0.537        0.898
   24     14       3    0.636  0.1026        0.403        0.799
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 89, number of events= 56 

                    coef exp(coef) se(coef)     z Pr(>|z|)
ctDNA.MRDPOSITIVE 0.4209    1.5234   0.2911 1.446    0.148

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     1.523     0.6564     0.861     2.695

Concordance= 0.54  (se = 0.032 )
Likelihood ratio test= 1.97  on 1 df,   p=0.2
Wald test            = 2.09  on 1 df,   p=0.1
Score (logrank) test = 2.12  on 1 df,   p=0.1
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.52 (0.86-2.7); p = 0.148"

#Association of ctDNA MRD MTM levels with clinicopathological factors

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

tally(~pN, data=circ_data, margins = TRUE)
pN
   N1    N2 Total 
   69    20    89 
circ_data$pN <- factor(circ_data$pN, levels = c("N1","N2"), labels = c("pN1 (n=69)","pN2 (n=20)"))
boxplot(ctDNA.MRD.MTM~pN, data=circ_data, main="ctDNA MRD MTM - pN", xlab="pN", ylab="MTM/mL", col="white",border="black", ylim=c(0, 10))

m1<-wilcox.test(ctDNA.MRD.MTM ~ pN, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.MRD.MTM by pN
W = 441.5, p-value = 0.001285
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -3.700528e-01 -2.534813e-05
sample estimates:
difference in location 
           -4.5909e-05 
tally(~ResMarg, data=circ_data, margins = TRUE)
ResMarg
   R0    R1 Total 
   60    29    89 
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0","R1"), labels = c("R0 (n=60)","R1 (n=29)"))
boxplot(ctDNA.MRD.MTM~ResMarg, data=circ_data, main="ctDNA MRD MTM - Resection Margin", 
        xlab="ResMarg", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))

m2 <- wilcox.test(ctDNA.MRD.MTM ~ ResMarg, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.MRD.MTM by ResMarg
W = 637.5, p-value = 0.007319
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.1700429119 -0.0000566823
sample estimates:
difference in location 
         -7.789783e-05 
tally(~PrimSite, data=circ_data, margins = TRUE)
PrimSite
 dCCA  pCCA Total 
   46    43    89 
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA","dCCA"), labels = c("pCCA (n=43)","dCCA (n=46)"))
boxplot(ctDNA.MRD.MTM~PrimSite, data=circ_data, main="ctDNA MRD MTM - Primary Site", 
        xlab="Primary Site", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))

m3 <- wilcox.test(ctDNA.MRD.MTM ~ PrimSite, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.MRD.MTM by PrimSite
W = 996, p-value = 0.9438
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.850159e-05  9.561474e-06
sample estimates:
difference in location 
          4.711544e-05 
tally(~Stage, data=circ_data, margins = TRUE)
Stage
   II   III    IV Total 
   38    39    12    89 
circ_data$ctDNA.MRD.MTM <- as.numeric(as.character(circ_data$ctDNA.MRD.MTM))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II","III","IV"), labels = c("II (n=37)", "III (n=40)","IV (n=12)"))
boxplot(ctDNA.MRD.MTM~Stage, data=circ_data, main="ctDNA MRD MTM - Stage", 
        xlab="Stage", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))

kruskal_result <- kruskal.test(ctDNA.MRD.MTM ~ Stage, data=circ_data)
print(kruskal_result)

    Kruskal-Wallis rank sum test

data:  ctDNA.MRD.MTM by Stage
Kruskal-Wallis chi-squared = 2.8384, df = 2, p-value = 0.2419
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.MRD.MTM, circ_data$Stage, 
                                        p.adjust.method = "BH", na.rm = TRUE)
Warning in wilcox.test.default(xi, xj, paired = paired, ...) :
  cannot compute exact p-value with ties
Warning in wilcox.test.default(xi, xj, paired = paired, ...) :
  cannot compute exact p-value with ties
Warning in wilcox.test.default(xi, xj, paired = paired, ...) :
  cannot compute exact p-value with ties
print(pairwise_wilcox)

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  circ_data$ctDNA.MRD.MTM and circ_data$Stage 

           II (n=37) III (n=40)
III (n=40) 0.46      -         
IV (n=12)  0.30      0.39      

P value adjustment method: BH 
tally(~CA19.MRD, data=circ_data, margins = TRUE)
CA19.MRD
Elevated   Normal    Total 
      15       74       89 
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal","Elevated"), labels = c("Normal (n=74)","Elevated (n=15)"))
boxplot(ctDNA.MRD.MTM~CA19.MRD, data=circ_data, main="ctDNA MRD MTM - CA 19-9", 
        xlab="CA 19-9", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))

m4 <- wilcox.test(ctDNA.MRD.MTM ~ CA19.MRD, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.MRD.MTM by CA19.MRD
W = 371.5, p-value = 0.00808
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.000269e-01 -4.645475e-05
sample estimates:
difference in location 
         -5.786662e-06 

#Median MTM/mL levels for ctDNA positive pts at MRD

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]

median_ctDNA <- median(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
range_ctDNA <- range(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
cat("Median MTM/mL post-surgery:", median_ctDNA, "\n")
Median MTM/mL post-surgery: 0.605 
cat("Range MTM/mL post-surgery:", range_ctDNA[1], "-", range_ctDNA[2], "\n")
Range MTM/mL post-surgery: 0.1 - 73.04 

#Association of ctDNA MRD status with clinicopathological factors

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$pN <- factor(circ_data$pN, levels = c("N1", "N2"), labels = c("pN1", "pN2"))
contingency_table <- table(circ_data$pN, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
Warning in chisq.test(contingency_table) :
  Chi-squared approximation may be incorrect
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 7.1944, df = 1, p-value = 0.007313
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.006385
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.403411 15.830714
sample estimates:
odds ratio 
  4.647239 
print(contingency_table)
     
      ctDNA(-) ctDNA(+)
  pN1       57       12
  pN2       10       10
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - pN", 
       x = "pN", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size


rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1"), labels = c("R0", "R1"))
contingency_table <- table(circ_data$ResMarg, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 5.1569, df = 1, p-value = 0.02315
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.01762
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.146895 10.855465
sample estimates:
odds ratio 
  3.472855 
print(contingency_table)
    
     ctDNA(-) ctDNA(+)
  R0       50       10
  R1       17       12
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Margins", 
       x = "Margins", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size


rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
contingency_table <- table(circ_data$PrimSite, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 0, df = 1, p-value = 1
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.3114469 2.6872455
sample estimates:
odds ratio 
 0.9152081 
print(contingency_table)
      
       ctDNA(-) ctDNA(+)
  pCCA       32       11
  dCCA       35       11
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Primary Site", 
       x = "Primary Site", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size


rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
contingency_table <- table(circ_data$Stage, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
Warning in chisq.test(contingency_table) :
  Chi-squared approximation may be incorrect
print(chi_square_test)

    Pearson's Chi-squared test

data:  contingency_table
X-squared = 2.68, df = 2, p-value = 0.2619
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.2546
alternative hypothesis: two.sided
print(contingency_table)
     
      ctDNA(-) ctDNA(+)
  II        31        7
  III       29       10
  IV         7        5
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Stage", 
       x = "Stage", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size


pairwise_fisher <- function(data, factor1, factor2) {
  levels <- unique(data[[factor1]])
  results <- data.frame(Stage1 = character(), Stage2 = character(), p.value = numeric(), stringsAsFactors = FALSE)
  
  for (i in 1:(length(levels) - 1)) {
    for (j in (i + 1):length(levels)) {
      subset_data <- data %>% filter(data[[factor1]] %in% c(levels[i], levels[j]))
      contingency_table_pairwise <- table(subset_data[[factor1]], subset_data[[factor2]])
      fisher_result <- fisher.test(contingency_table_pairwise)
      results <- rbind(results, data.frame(Stage1 = levels[i], Stage2 = levels[j], p.value = fisher_result$p.value))
    }
  }
  return(results)
}

# Perform pairwise comparisons
pairwise_results <- pairwise_fisher(circ_data, "Stage", "ctDNA.MRD")
print(pairwise_results)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal", "Elevated"), labels = c("Normal", "Elevated"))
contingency_table <- table(circ_data$CA19.MRD, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
Warning in chisq.test(contingency_table) :
  Chi-squared approximation may be incorrect
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 6.1961, df = 1, p-value = 0.0128
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.008766
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.282588 18.543083
sample estimates:
odds ratio 
  4.787433 
print(contingency_table)
          
           ctDNA(-) ctDNA(+)
  Normal         60       14
  Elevated        7        8
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - CA 19-9", 
       x = "CA 19-9", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#Prognostic role of ctDNA at C5D1

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.C5D1, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.C5D1=NEGATIVE 71     48   15.9   12.92   27.57
ctDNA.C5D1=POSITIVE 17     17    4.8    3.09    7.86
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C5D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C5D1, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.C5D1=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     43      27    0.615  0.0581        0.491        0.717
   24     30      12    0.441  0.0596        0.323        0.553

                ctDNA.C5D1=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    12.00000      1.00000     16.00000      0.05882      0.05707      0.00391      0.23501 
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C5D1, data = circ_data)

  n= 88, number of events= 65 

                     coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C5D1POSITIVE 2.0439    7.7207   0.3238 6.312 2.76e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                   exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1POSITIVE     7.721     0.1295     4.093     14.56

Concordance= 0.638  (se = 0.026 )
Likelihood ratio test= 31.63  on 1 df,   p=2e-08
Wald test            = 39.84  on 1 df,   p=3e-10
Score (logrank) test = 53.01  on 1 df,   p=3e-13
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 7.72 (4.09-14.56); p = 0"

#Multivariate regression model for DFS with ctDNA and CA 19-9

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
circ_datadf$Sex <- factor(circ_datadf$Sex, levels = c("Female", "Male"), labels = c("Female", "Male"))
circ_datadf$PrimSite <- factor(circ_datadf$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
circ_datadf$Chemo <- factor(circ_datadf$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis"))
circ_datadf$ResMarg <- factor(circ_datadf$ResMarg, levels = c("R0", "R1"))
circ_datadf$Stage <- factor(circ_datadf$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
circ_datadf$TP53 <- factor(circ_datadf$TP53, levels = c("WT", "Mut"))
circ_datadf$CA19.C5D1 <- factor(circ_datadf$CA19.C5D1, levels = c("Normal", "Elevated"))
circ_datadf$ctDNA.C5D1 <- factor(circ_datadf$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
surv_object<-Surv(time = circ_datadf$RFS.months, event = circ_datadf$RFS.Event) 
cox_fit <- coxph(surv_object ~ Sex + Age + PrimSite + Stage + Chemo + ResMarg + CA19.C5D1 + ctDNA.C5D1, data=circ_datadf) 
ggforest(cox_fit, data = circ_datadf, main = "Multivariate Regression Model for DFS - Landmark analysis", refLabel = "Reference Group")

test.ph <- cox.zph(cox_fit)

#Univariate regression model for factors used at the C5D1 MVA

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Sex <- factor(circ_data$Sex, levels = c("Female", "Male"), labels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Sex, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ Sex, data = circ_data)

  n= 89, number of events= 65 

          coef exp(coef) se(coef)     z Pr(>|z|)
SexMale 0.1594    1.1728   0.2689 0.593    0.553

        exp(coef) exp(-coef) lower .95 upper .95
SexMale     1.173     0.8527    0.6923     1.987

Concordance= 0.515  (se = 0.031 )
Likelihood ratio test= 0.36  on 1 df,   p=0.5
Wald test            = 0.35  on 1 df,   p=0.6
Score (logrank) test = 0.35  on 1 df,   p=0.6
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.17 (0.69-1.99); p = 0.553"
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
cox_fit <- coxph(surv_object ~ Age, data=circ_data) #univariate for age
summary(cox_fit)
Call:
coxph(formula = surv_object ~ Age, data = circ_data)

  n= 89, number of events= 65 

        coef exp(coef) se(coef)     z Pr(>|z|)
Age 0.002393  1.002396 0.019228 0.124    0.901

    exp(coef) exp(-coef) lower .95 upper .95
Age     1.002     0.9976    0.9653     1.041

Concordance= 0.493  (se = 0.041 )
Likelihood ratio test= 0.02  on 1 df,   p=0.9
Wald test            = 0.02  on 1 df,   p=0.9
Score (logrank) test = 0.02  on 1 df,   p=0.9
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1 (0.97-1.04); p = 0.901"
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA")) #univariate for Primary Site
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ PrimSite, data = circ_data)

  n= 89, number of events= 65 

                coef exp(coef) se(coef)      z Pr(>|z|)  
PrimSitedCCA -0.4558    0.6340   0.2520 -1.809   0.0705 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

             exp(coef) exp(-coef) lower .95 upper .95
PrimSitedCCA     0.634      1.577    0.3869     1.039

Concordance= 0.535  (se = 0.034 )
Likelihood ratio test= 3.31  on 1 df,   p=0.07
Wald test            = 3.27  on 1 df,   p=0.07
Score (logrank) test = 3.33  on 1 df,   p=0.07
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.63 (0.39-1.04); p = 0.071"
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ Stage, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=3, palette=c("blue","green","red"), title="DFS - ctDNA C5D1 - Stage", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("II", "III", "IV"), legend.title="")

circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV")) #univariate for Stage
cox_fit <- coxph(surv_object ~ Stage, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ Stage, data = circ_data)

  n= 89, number of events= 65 

           coef exp(coef) se(coef)     z Pr(>|z|)   
StageIII 0.2297    1.2583   0.2777 0.827  0.40805   
StageIV  1.1237    3.0761   0.3631 3.095  0.00197 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

         exp(coef) exp(-coef) lower .95 upper .95
StageIII     1.258     0.7947    0.7301     2.168
StageIV      3.076     0.3251    1.5099     6.267

Concordance= 0.565  (se = 0.037 )
Likelihood ratio test= 8.34  on 2 df,   p=0.02
Wald test            = 9.93  on 2 df,   p=0.007
Score (logrank) test = 10.77  on 2 df,   p=0.005
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis")) #univariate for Chemotherapy
cox_fit <- coxph(surv_object ~ Chemo, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ Chemo, data = circ_data)

  n= 89, number of events= 65 

                coef exp(coef) se(coef)      z Pr(>|z|)
ChemoGemCis -0.02437   0.97592  0.24846 -0.098    0.922

            exp(coef) exp(-coef) lower .95 upper .95
ChemoGemCis    0.9759      1.025    0.5997     1.588

Concordance= 0.507  (se = 0.034 )
Likelihood ratio test= 0.01  on 1 df,   p=0.9
Wald test            = 0.01  on 1 df,   p=0.9
Score (logrank) test = 0.01  on 1 df,   p=0.9
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.98 (0.6-1.59); p = 0.922"
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1")) #univariate for Resection margin
cox_fit <- coxph(surv_object ~ ResMarg, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ ResMarg, data = circ_data)

  n= 89, number of events= 65 

            coef exp(coef) se(coef)     z Pr(>|z|)
ResMargR1 0.3415    1.4071   0.2617 1.305    0.192

          exp(coef) exp(-coef) lower .95 upper .95
ResMargR1     1.407     0.7107    0.8425      2.35

Concordance= 0.534  (se = 0.03 )
Likelihood ratio test= 1.64  on 1 df,   p=0.2
Wald test            = 1.7  on 1 df,   p=0.2
Score (logrank) test = 1.72  on 1 df,   p=0.2
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.41 (0.84-2.35); p = 0.192"
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$CA19.C5D1 <- factor(circ_data$CA19.C5D1, levels = c("Normal", "Elevated")) #univariate for CA 19-9 C5D1
cox_fit <- coxph(surv_object ~ CA19.C5D1, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ CA19.C5D1, data = circ_data)

  n= 80, number of events= 56 
   (9 observations deleted due to missingness)

                    coef exp(coef) se(coef)     z Pr(>|z|)
CA19.C5D1Elevated 0.2218    1.2483   0.3264 0.679    0.497

                  exp(coef) exp(-coef) lower .95 upper .95
CA19.C5D1Elevated     1.248     0.8011    0.6584     2.367

Concordance= 0.535  (se = 0.032 )
Likelihood ratio test= 0.44  on 1 df,   p=0.5
Wald test            = 0.46  on 1 df,   p=0.5
Score (logrank) test = 0.46  on 1 df,   p=0.5
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.25 (0.66-2.37); p = 0.497"
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")) #univariate for ctDNA C5D1
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data)
summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C5D1, data = circ_data)

  n= 88, number of events= 65 
   (1 observation deleted due to missingness)

                     coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C5D1Positive 2.0439    7.7207   0.3238 6.312 2.76e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                   exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1Positive     7.721     0.1295     4.093     14.56

Concordance= 0.638  (se = 0.026 )
Likelihood ratio test= 31.63  on 1 df,   p=2e-08
Wald test            = 39.84  on 1 df,   p=3e-10
Score (logrank) test = 53.01  on 1 df,   p=3e-13
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 7.72 (4.09-14.56); p = 0"

#OS by ctDNA at C5D1

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C5D1, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.C5D1, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.C5D1=NEGATIVE 71     39   44.8    31.7      NA
ctDNA.C5D1=POSITIVE 17     17   18.4    13.1    35.3
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C5D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C5D1, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.C5D1=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     69       2    0.972  0.0196        0.892        0.993
   24     56      13    0.789  0.0484        0.674        0.867

                ctDNA.C5D1=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     12       5    0.706   0.111        0.431        0.866
   24      7       5    0.412   0.119        0.186        0.626
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C5D1, data = circ_data)

  n= 88, number of events= 56 

                     coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C5D1POSITIVE 1.2634    3.5375   0.2983 4.235 2.29e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                   exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1POSITIVE     3.537     0.2827     1.971     6.348

Concordance= 0.603  (se = 0.029 )
Likelihood ratio test= 14.98  on 1 df,   p=1e-04
Wald test            = 17.93  on 1 df,   p=2e-05
Score (logrank) test = 20.38  on 1 df,   p=6e-06
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 3.54 (1.97-6.35); p = 0"

#Prognostic role of ctDNA at C8D1

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.C8D1, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.C8D1=NEGATIVE 62     40  17.02   13.84    33.4
ctDNA.C8D1=POSITIVE 15     15   7.13    5.36    15.2
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C8D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C8D1, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.C8D1=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     40      22    0.645  0.0608        0.513        0.750
   24     29      10    0.481  0.0637        0.353        0.599

                ctDNA.C8D1=POSITIVE 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     12.0000       3.0000      12.0000       0.2000       0.1033       0.0489       0.4239 
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C8D1, data = circ_data)

  n= 77, number of events= 55 

                     coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C8D1POSITIVE 1.6561    5.2389   0.3281 5.047 4.49e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                   exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1POSITIVE     5.239     0.1909     2.754     9.967

Concordance= 0.624  (se = 0.028 )
Likelihood ratio test= 20.53  on 1 df,   p=6e-06
Wald test            = 25.47  on 1 df,   p=4e-07
Score (logrank) test = 31.38  on 1 df,   p=2e-08
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 5.24 (2.75-9.97); p = 0"

#OS by ctDNA at C8D1

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C8D1, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.C8D1, data = circ_data)

                     n events median 0.95LCL 0.95UCL
ctDNA.C8D1=NEGATIVE 62     32   48.4    30.1      NA
ctDNA.C8D1=POSITIVE 15     15   29.8    17.5    41.8
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C8D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C8D1, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.C8D1=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     60       2    0.968  0.0224        0.877        0.992
   24     48      12    0.774  0.0531        0.649        0.860

                ctDNA.C8D1=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       2    0.867  0.0878        0.564        0.965
   24      8       5    0.533  0.1288        0.263        0.744
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C8D1, data = circ_data)

  n= 77, number of events= 47 

                     coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.C8D1POSITIVE 1.0300    2.8011   0.3192 3.227  0.00125 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                   exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1POSITIVE     2.801      0.357     1.499     5.236

Concordance= 0.574  (se = 0.031 )
Likelihood ratio test= 9.08  on 1 df,   p=0.003
Wald test            = 10.42  on 1 df,   p=0.001
Score (logrank) test = 11.35  on 1 df,   p=8e-04
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 2.8 (1.5-5.24); p = 0.001"

#Prognostic role of ctDNA anytime post-surgery

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.anytime, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.anytime, data = circ_data)

                        n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 42     20  38.71   19.06      NA
ctDNA.anytime=POSITIVE 47     45   8.05    7.56      12
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA anytime post-surgery", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.anytime=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     30      11    0.734  0.0689        0.571        0.843
   24     24       5    0.609  0.0766        0.442        0.739

                ctDNA.anytime=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     15      32    0.319  0.0680       0.1928        0.453
   24      7       8    0.149  0.0519       0.0655        0.264
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)

  n= 89, number of events= 65 

                        coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.anytimePOSITIVE 1.3374    3.8090   0.2757 4.851 1.23e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE     3.809     0.2625     2.219     6.538

Concordance= 0.649  (se = 0.03 )
Likelihood ratio test= 26  on 1 df,   p=3e-07
Wald test            = 23.54  on 1 df,   p=1e-06
Score (logrank) test = 26.68  on 1 df,   p=2e-07
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 3.81 (2.22-6.54); p = 0"

#OS by ctDNA anytime post-surgery

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.anytime, data = circ_data)

                        n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 42     15     NA    48.4      NA
ctDNA.anytime=POSITIVE 47     41   27.6    21.3    35.3
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA anytime post-surgery", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.anytime=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     41       1    0.976  0.0235        0.843        0.997
   24     38       3    0.905  0.0453        0.766        0.963

                ctDNA.anytime=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     41       6    0.872  0.0487        0.738        0.941
   24     26      15    0.553  0.0725        0.401        0.681
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)

  n= 89, number of events= 56 

                        coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.anytimePOSITIVE 1.3272    3.7706   0.3039 4.367 1.26e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE     3.771     0.2652     2.078     6.841

Concordance= 0.648  (se = 0.033 )
Likelihood ratio test= 21.97  on 1 df,   p=3e-06
Wald test            = 19.07  on 1 df,   p=1e-05
Score (logrank) test = 21.89  on 1 df,   p=3e-06
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 3.77 (2.08-6.84); p = 0"

#Prognostic role of ctDNA Dynamics

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 48] <- 48
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.Dynamics, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.Dynamics, data = circ_data)

                                      n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=Converted Negative    12     10  13.57    8.94      NA
ctDNA.Dynamics=Converted Positive    11     11   5.43    4.60      NA
ctDNA.Dynamics=Persistently Negative 56     34  19.06   13.84      NA
ctDNA.Dynamics=Persistently Positive 10     10   5.10    2.40      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) 
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="ctDNA Dynamics | Pre-treatment - On-treatment", ylab= "Disease Free Survival", xlab="Time from Surgery (Months)", legend.title="") 

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Dynamics=Converted Negative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       6    0.500   0.144        0.208        0.736
   24      5       1    0.417   0.142        0.152        0.665

                ctDNA.Dynamics=Converted Positive 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     12.0000       2.0000       9.0000       0.1818       0.1163       0.0285       0.4417 

                ctDNA.Dynamics=Persistently Negative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     36      19    0.656  0.0640        0.515        0.765
   24     26       9    0.489  0.0677        0.351        0.613

                ctDNA.Dynamics=Persistently Positive 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
    12.00000      1.00000      9.00000      0.10000      0.09487      0.00572      0.35813 
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)  
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)

  n= 89, number of events= 65 

                                      coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.DynamicsConverted Negative    0.3528    1.4230   0.3607 0.978    0.328    
ctDNA.DynamicsConverted Positive    1.7567    5.7935   0.3684 4.768 1.86e-06 ***
ctDNA.DynamicsPersistently Positive 1.9013    6.6944   0.3869 4.914 8.94e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                    exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsConverted Negative        1.423     0.7027    0.7017     2.886
ctDNA.DynamicsConverted Positive        5.794     0.1726    2.8140    11.928
ctDNA.DynamicsPersistently Positive     6.694     0.1494    3.1358    14.291

Concordance= 0.659  (se = 0.031 )
Likelihood ratio test= 30.66  on 3 df,   p=1e-06
Wald test            = 35.98  on 3 df,   p=8e-08
Score (logrank) test = 44.86  on 3 df,   p=1e-09

#OS ctDNA Dynamics

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.Dynamics, data = circ_data)

                                      n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=Converted Negative    12      7   52.7   27.57      NA
ctDNA.Dynamics=Converted Positive    11     11   21.3   16.17      NA
ctDNA.Dynamics=Persistently Negative 56     28   48.4   29.28      NA
ctDNA.Dynamics=Persistently Positive 10     10   24.0    9.66      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event) 
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("purple","green","blue","red"), title="MRD Dynamics | Pre-treatment - On-treatment", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.title="") 

summary(KM_curve, times= c(12, 24))
Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Dynamics=Converted Negative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     11       1    0.917  0.0798        0.539        0.988
   24      9       2    0.750  0.1250        0.408        0.912

                ctDNA.Dynamics=Converted Positive 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     10       1    0.909  0.0867        0.508        0.987
   24      5       5    0.455  0.1501        0.167        0.707

                ctDNA.Dynamics=Persistently Negative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     55       1    0.982  0.0177        0.880        0.997
   24     45      10    0.804  0.0531        0.673        0.886

                ctDNA.Dynamics=Persistently Positive 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       4      0.6   0.155        0.253        0.827
   24      5       1      0.5   0.158        0.184        0.753
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)  
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)

  n= 89, number of events= 56 

                                       coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.DynamicsConverted Negative    0.07171   1.07435  0.42352 0.169 0.865537    
ctDNA.DynamicsConverted Positive    1.10013   3.00455  0.36133 3.045 0.002329 ** 
ctDNA.DynamicsPersistently Positive 1.37436   3.95256  0.37891 3.627 0.000287 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                    exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsConverted Negative        1.074     0.9308    0.4684     2.464
ctDNA.DynamicsConverted Positive        3.005     0.3328    1.4799     6.100
ctDNA.DynamicsPersistently Positive     3.953     0.2530    1.8808     8.306

Concordance= 0.61  (se = 0.035 )
Likelihood ratio test= 16.29  on 3 df,   p=0.001
Wald test            = 18.53  on 3 df,   p=3e-04
Score (logrank) test = 21  on 3 df,   p=1e-04

#OP for pts converted positive during ACT

setwd("~/Downloads") 
clinstage<- read.csv("ASAN_Cholangio_OP.csv")
clinstage_df<- as.data.frame(clinstage)
clinstage_df <- clinstage_df[clinstage_df$ctDNA.Dynamics=="Converted Positive",]

##Overview plot - stratified by Stage
oplot_stratify <-swimmer_plot(df=clinstage_df,
                              id='PatientName',
                              end='fu.diff.months',
                              #name_fill='Arm',
                              col="gray",
                              alpha=0.75,
                              width=.01,
                              base_size = 14)
oplot_stratify <- oplot_stratify + theme(panel.border = element_blank())
oplot_stratify <- oplot_stratify + scale_y_continuous(breaks = seq(0, 108, by = 6))
oplot_stratify <- oplot_stratify + labs(x ="Patients" , y="Months from Surgery")
oplot_stratify


##plot events
oplot_ev3 <- oplot_stratify + swimmer_points(df_points=clinstage_df,
                                             id='PatientName',
                                             time='date.diff.months',
                                             name_shape ='Event_type',
                                             name_col = 'Event',
                                             size=3.5,fill='black',
                                             #col='darkgreen'
)
oplot_ev3
Warning: The shape palette can deal with a maximum of 6 discrete values because more than 6 becomes difficult to discriminate
ℹ you have requested 9 values. Consider specifying shapes manually if you need that many have them.
Warning: Removed 55 rows containing missing values or values outside the scale range (`geom_point()`).

#Shape customization to Event_type

oplot_ev3.1 <- oplot_ev3 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4, 5, 23, 7, 15),breaks=c('ctDNA_neg', 'ctDNA_pos', 'Imaging', 'Surgery', 'Death', "cea_neg", "cea_pos", "ca19_neg", "ca19_pos"))

oplot_ev3.1
Warning: Removed 11 rows containing missing values or values outside the scale range (`geom_point()`).

#plot treatment

oplot_ev4 <- oplot_ev3.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev4 <- oplot_ev4 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev4  
Warning: Removed 11 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 133 rows containing missing values or values outside the scale range (`geom_segment()`).

#colour customization
# orange=ACT, Black=Death, Red=PD, ctDNA negative=white, ctDNA positive=black, Surgery=blue, TURBT=gray 
oplot_ev4.2 <- oplot_ev4 + ggplot2::scale_color_manual(name="Event",values=c( "orange", "purple", "blue", "black", "black", "red", "blue", "blue"))
oplot_ev4.2
Warning: Removed 11 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 133 rows containing missing values or values outside the scale range (`geom_segment()`).

#ctDNA clearance proportions by chemotherapy regimen

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]

circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels = c("Converted Negative", "Persistently Positive"), labels = c("Clearance", "No Clearance"))
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"))
contingency_table <- table(circ_data$Chemo, circ_data$ctDNA.Dynamics)
chi_square_test <- chisq.test(contingency_table)
Warning in chisq.test(contingency_table) :
  Chi-squared approximation may be incorrect
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 0.26481, df = 1, p-value = 0.6068
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.4149
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  0.3023403 20.3968347
sample estimates:
odds ratio 
   2.24368 
print(contingency_table)
        
         Clearance No Clearance
  CAP            6            3
  GemCis         6            7
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance by Regimen", 
       x = "Regimen", 
       y = "Patients (%)", 
       fill = "ctDNA Clearance",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Clearance" = "blue", "No Clearance" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#Prognostic role of ctDNA C5D1 on Chemotherapy - 4 groups

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C5D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C5D1.Chemo = case_when(
    Chemo == "CAP" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
    Chemo == "CAP" & ctDNA.C5D1 == "POSITIVE" ~ 2,
    Chemo == "GemCis" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
    Chemo == "GemCis" & ctDNA.C5D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.Chemo, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.C5D1.Chemo, data = circ_data)

   1 observation deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
ctDNA.C5D1.Chemo=1 36     24  15.58   11.57      NA
ctDNA.C5D1.Chemo=2  8      8   3.95    2.27      NA
ctDNA.C5D1.Chemo=3 35     24  16.79   11.96    38.7
ctDNA.C5D1.Chemo=4  9      9   4.83    3.59      NA
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1.Chemo) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")

summary(KM_curve, times= c(0, 12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C5D1.Chemo, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

1 observation deleted due to missingness 
                ctDNA.C5D1.Chemo=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     36       0    1.000  0.0000        1.000        1.000
   12     21      14    0.602  0.0827        0.422        0.741
   24     14       7    0.401  0.0829        0.241        0.556

                ctDNA.C5D1.Chemo=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
           0            8            0            1            0            1            1 

                ctDNA.C5D1.Chemo=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     35       0    1.000  0.0000        1.000        1.000
   12     22      13    0.629  0.0817        0.448        0.765
   24     16       5    0.481  0.0851        0.309        0.634

                ctDNA.C5D1.Chemo=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      9       0    1.000   0.000      1.00000        1.000
   12      1       8    0.111   0.105      0.00613        0.388
circ_data$ctDNA.C5D1.Chemo <- factor(circ_data$ctDNA.C5D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.Chemo, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C5D1.Chemo, data = circ_data)

  n= 88, number of events= 65 
   (1 observation deleted due to missingness)

                      coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C5D1.Chemo2  2.34287  10.41111  0.45548 5.144 2.69e-07 ***
ctDNA.C5D1.Chemo3  0.00918   1.00922  0.28914 0.032    0.975    
ctDNA.C5D1.Chemo4  1.87019   6.48950  0.41611 4.494 6.97e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1.Chemo2    10.411    0.09605    4.2637    25.422
ctDNA.C5D1.Chemo3     1.009    0.99086    0.5726     1.779
ctDNA.C5D1.Chemo4     6.490    0.15409    2.8709    14.669

Concordance= 0.639  (se = 0.035 )
Likelihood ratio test= 32.54  on 3 df,   p=4e-07
Wald test            = 40.73  on 3 df,   p=7e-09
Score (logrank) test = 55.87  on 3 df,   p=4e-12
cox_fit_summary <- summary(cox_fit)

#Prognostic role of ctDNA C8D1 on Chemotherapy - 4 groups

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C8D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C8D1.Chemo = case_when(
    Chemo == "CAP" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
    Chemo == "CAP" & ctDNA.C8D1 == "POSITIVE" ~ 2,
    Chemo == "GemCis" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
    Chemo == "GemCis" & ctDNA.C8D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.Chemo, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.C8D1.Chemo, data = circ_data)

   12 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
ctDNA.C8D1.Chemo=1 31     20  15.84   11.57      NA
ctDNA.C8D1.Chemo=2  6      6   7.86    5.43      NA
ctDNA.C8D1.Chemo=3 31     20  26.98   13.51      NA
ctDNA.C8D1.Chemo=4  9      9   6.44    4.83      NA
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1.Chemo) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")

summary(KM_curve, times= c(0, 12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C8D1.Chemo, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

12 observations deleted due to missingness 
                ctDNA.C8D1.Chemo=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     31       0    1.000  0.0000        1.000        1.000
   12     19      12    0.613  0.0875        0.420        0.758
   24     13       6    0.419  0.0886        0.247        0.583

                ctDNA.C8D1.Chemo=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      6       0    1.000   0.000      1.00000        1.000
   12      1       5    0.167   0.152      0.00772        0.517

                ctDNA.C8D1.Chemo=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     31       0    1.000   0.000        1.000        1.000
   12     21      10    0.677   0.084        0.484        0.812
   24     16       4    0.545   0.090        0.355        0.700

                ctDNA.C8D1.Chemo=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      9       0    1.000   0.000       1.0000        1.000
   12      2       7    0.222   0.139       0.0337        0.513
circ_data$ctDNA.C8D1.Chemo <- factor(circ_data$ctDNA.C8D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.Chemo, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C8D1.Chemo, data = circ_data)

  n= 77, number of events= 55 
   (12 observations deleted due to missingness)

                      coef exp(coef) se(coef)      z Pr(>|z|)    
ctDNA.C8D1.Chemo2  1.59481   4.92738  0.48599  3.282  0.00103 ** 
ctDNA.C8D1.Chemo3 -0.06279   0.93914  0.31645 -0.198  0.84271    
ctDNA.C8D1.Chemo4  1.64389   5.17525  0.41910  3.922 8.76e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1.Chemo2    4.9274     0.2029    1.9008    12.773
ctDNA.C8D1.Chemo3    0.9391     1.0648    0.5051     1.746
ctDNA.C8D1.Chemo4    5.1753     0.1932    2.2761    11.767

Concordance= 0.638  (se = 0.037 )
Likelihood ratio test= 20.58  on 3 df,   p=1e-04
Wald test            = 25.53  on 3 df,   p=1e-05
Score (logrank) test = 31.47  on 3 df,   p=7e-07
cox_fit_summary <- summary(cox_fit)

#Prognostic role of ctDNA C5D1 on Primary Site - 4 groups

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C5D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C5D1.PrimSite = case_when(
    PrimSite == "dCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
    PrimSite == "dCCA" & ctDNA.C5D1 == "POSITIVE" ~ 2,
    PrimSite == "pCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
    PrimSite == "pCCA" & ctDNA.C5D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.PrimSite, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.C5D1.PrimSite, data = circ_data)

   1 observation deleted due to missingness 
                       n events median 0.95LCL 0.95UCL
ctDNA.C5D1.PrimSite=1 37     20  16.79   11.96      NA
ctDNA.C5D1.PrimSite=2  8      8   3.88    2.27      NA
ctDNA.C5D1.PrimSite=3 34     28  15.71   11.63    27.3
ctDNA.C5D1.PrimSite=4  9      9   4.80    3.59      NA
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1.PrimSite) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")

summary(KM_curve, times= c(0, 12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

1 observation deleted due to missingness 
                ctDNA.C5D1.PrimSite=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     37       0    1.000  0.0000        1.000        1.000
   12     22      14    0.613  0.0811        0.435        0.749
   24     17       4    0.498  0.0838        0.327        0.648

                ctDNA.C5D1.PrimSite=2 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
           0            8            0            1            0            1            1 

                ctDNA.C5D1.PrimSite=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     34       0    1.000  0.0000        1.000        1.000
   12     21      13    0.618  0.0833        0.434        0.757
   24     13       8    0.382  0.0833        0.223        0.540

                ctDNA.C5D1.PrimSite=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      9       0    1.000   0.000      1.00000        1.000
   12      1       8    0.111   0.105      0.00613        0.388
circ_data$ctDNA.C5D1.PrimSite <- factor(circ_data$ctDNA.C5D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.PrimSite, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data)

  n= 88, number of events= 65 
   (1 observation deleted due to missingness)

                        coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C5D1.PrimSite2  2.4864   12.0174   0.4637 5.362 8.24e-08 ***
ctDNA.C5D1.PrimSite3  0.5269    1.6937   0.2948 1.787   0.0739 .  
ctDNA.C5D1.PrimSite4  2.2230    9.2352   0.4307 5.162 2.45e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                     exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1.PrimSite2    12.017    0.08321    4.8428    29.821
ctDNA.C5D1.PrimSite3     1.694    0.59042    0.9503     3.019
ctDNA.C5D1.PrimSite4     9.235    0.10828    3.9706    21.480

Concordance= 0.665  (se = 0.035 )
Likelihood ratio test= 35.17  on 3 df,   p=1e-07
Wald test            = 41.85  on 3 df,   p=4e-09
Score (logrank) test = 56.32  on 3 df,   p=4e-12
cox_fit_summary <- summary(cox_fit)

#Prognostic role of ctDNA C8D1 on Primary Site - 4 groups

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C8D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C8D1.PrimSite = case_when(
    PrimSite == "dCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
    PrimSite == "dCCA" & ctDNA.C8D1 == "POSITIVE" ~ 2,
    PrimSite == "pCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
    PrimSite == "pCCA" & ctDNA.C8D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.PrimSite, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~ 
    ctDNA.C8D1.PrimSite, data = circ_data)

   12 observations deleted due to missingness 
                       n events median 0.95LCL 0.95UCL
ctDNA.C8D1.PrimSite=1 31     15  38.71   13.51      NA
ctDNA.C8D1.PrimSite=2  8      8   7.50    6.44      NA
ctDNA.C8D1.PrimSite=3 31     25  15.84   10.98    27.6
ctDNA.C8D1.PrimSite=4  7      7   5.43    4.60      NA
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1.PrimSite) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")

summary(KM_curve, times= c(0, 12, 24))
Call: survfit(formula = surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

12 observations deleted due to missingness 
                ctDNA.C8D1.PrimSite=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     31       0    1.000   0.000        1.000        1.000
   12     21      10    0.677   0.084        0.484        0.812
   24     17       3    0.579   0.089        0.387        0.730

                ctDNA.C8D1.PrimSite=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      8       0    1.000   0.000      1.00000        1.000
   12      1       7    0.125   0.117      0.00659        0.423

                ctDNA.C8D1.PrimSite=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     31       0    1.000  0.0000         1.00        1.000
   12     19      12    0.613  0.0875         0.42        0.758
   24     12       7    0.387  0.0875         0.22        0.551

                ctDNA.C8D1.PrimSite=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      7       0    1.000   0.000       1.0000        1.000
   12      2       5    0.286   0.171       0.0411        0.612
circ_data$ctDNA.C8D1.PrimSite <- factor(circ_data$ctDNA.C8D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.PrimSite, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data)

  n= 77, number of events= 55 
   (12 observations deleted due to missingness)

                       coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.C8D1.PrimSite2 2.1501    8.5860   0.4665 4.609 4.05e-06 ***
ctDNA.C8D1.PrimSite3 0.7532    2.1239   0.3301 2.282   0.0225 *  
ctDNA.C8D1.PrimSite4 2.0206    7.5431   0.4794 4.215 2.50e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                     exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1.PrimSite2     8.586     0.1165     3.441    21.424
ctDNA.C8D1.PrimSite3     2.124     0.4708     1.112     4.056
ctDNA.C8D1.PrimSite4     7.543     0.1326     2.948    19.303

Concordance= 0.671  (se = 0.036 )
Likelihood ratio test= 26.02  on 3 df,   p=9e-06
Wald test            = 28.78  on 3 df,   p=2e-06
Score (logrank) test = 35.81  on 3 df,   p=8e-08
cox_fit_summary <- summary(cox_fit)
---
title: "ASAN Cholangio_GL_Final analysis 092024"
output: html_notebook
---
library(swimplot)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(ComplexHeatmap)
library(rms)

#ctDNA positivity by stage and window
```{r}
#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA C5D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C5D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C5D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA C8D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C8D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C8D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA EOT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.EOT!="",]
circ_data$ctDNA.EOT <- factor(circ_data$ctDNA.EOT, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.EOT %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.EOT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.EOT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.EOT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA anytime
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```


#Heatmap for clinicopathologic factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Sex = circ_data$Sex,
  PrimSite = circ_data$PrimSite,
  pT = circ_data$pT,
  Pathology = circ_data$Pathology,
  Chemo = circ_data$Chemo,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.C5D1 = circ_data$ctDNA.C5D1,
  ctDNA.C8D1 = circ_data$ctDNA.C8D1,
  ctDNA.EOT = circ_data$ctDNA.EOT,
  ctDNA.anytime = circ_data$ctDNA.anytime,
  RecStatus = circ_data$RecStatus,
  VitalStatus = circ_data$VitalStatus,
  
    col = list(Stage = c("II" = "seagreen2", "III" = "orange", "IV" = "purple"),
    Sex = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("pCCA" = "darkgreen", "dCCA" ="#008BCE"),
    pT = c("T1" = "lightblue", "T2" ="orange", "T3" = "brown" ),
    Pathology = c("G1" = "yellow3", "G2" ="darkgreen", "G3" = "brown2"),
    Chemo = c("CAP" = "lightblue", "GemCis" = "orange2"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C5D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C8D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.EOT = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    RFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
    OS.Event = c("TRUE" = "black", "FALSE" ="grey")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#Prognostic role of ctDNA at the MRD time point
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA MRD timepoint", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA at the MRD time point
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MRD timepoint", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Association of ctDNA MRD MTM levels with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

tally(~pN, data=circ_data, margins = TRUE)
circ_data$pN <- factor(circ_data$pN, levels = c("N1","N2"), labels = c("pN1 (n=69)","pN2 (n=20)"))
boxplot(ctDNA.MRD.MTM~pN, data=circ_data, main="ctDNA MRD MTM - pN", xlab="pN", ylab="MTM/mL", col="white",border="black", ylim=c(0, 10))
m1<-wilcox.test(ctDNA.MRD.MTM ~ pN, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

tally(~ResMarg, data=circ_data, margins = TRUE)
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0","R1"), labels = c("R0 (n=60)","R1 (n=29)"))
boxplot(ctDNA.MRD.MTM~ResMarg, data=circ_data, main="ctDNA MRD MTM - Resection Margin", 
        xlab="ResMarg", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m2 <- wilcox.test(ctDNA.MRD.MTM ~ ResMarg, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

tally(~PrimSite, data=circ_data, margins = TRUE)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA","dCCA"), labels = c("pCCA (n=43)","dCCA (n=46)"))
boxplot(ctDNA.MRD.MTM~PrimSite, data=circ_data, main="ctDNA MRD MTM - Primary Site", 
        xlab="Primary Site", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m3 <- wilcox.test(ctDNA.MRD.MTM ~ PrimSite, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

tally(~Stage, data=circ_data, margins = TRUE)
circ_data$ctDNA.MRD.MTM <- as.numeric(as.character(circ_data$ctDNA.MRD.MTM))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II","III","IV"), labels = c("II (n=37)", "III (n=40)","IV (n=12)"))
boxplot(ctDNA.MRD.MTM~Stage, data=circ_data, main="ctDNA MRD MTM - Stage", 
        xlab="Stage", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
kruskal_result <- kruskal.test(ctDNA.MRD.MTM ~ Stage, data=circ_data)
print(kruskal_result)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.MRD.MTM, circ_data$Stage, 
                                        p.adjust.method = "BH", na.rm = TRUE)
print(pairwise_wilcox)

tally(~CA19.MRD, data=circ_data, margins = TRUE)
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal","Elevated"), labels = c("Normal (n=74)","Elevated (n=15)"))
boxplot(ctDNA.MRD.MTM~CA19.MRD, data=circ_data, main="ctDNA MRD MTM - CA 19-9", 
        xlab="CA 19-9", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m4 <- wilcox.test(ctDNA.MRD.MTM ~ CA19.MRD, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)
```


#Median MTM/mL levels for ctDNA positive pts at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]

median_ctDNA <- median(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
range_ctDNA <- range(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
cat("Median MTM/mL post-surgery:", median_ctDNA, "\n")
cat("Range MTM/mL post-surgery:", range_ctDNA[1], "-", range_ctDNA[2], "\n")
```


#Association of ctDNA MRD status with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$pN <- factor(circ_data$pN, levels = c("N1", "N2"), labels = c("pN1", "pN2"))
contingency_table <- table(circ_data$pN, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - pN", 
       x = "pN", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1"), labels = c("R0", "R1"))
contingency_table <- table(circ_data$ResMarg, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Margins", 
       x = "Margins", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
contingency_table <- table(circ_data$PrimSite, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Primary Site", 
       x = "Primary Site", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
contingency_table <- table(circ_data$Stage, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Stage", 
       x = "Stage", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

pairwise_fisher <- function(data, factor1, factor2) {
  levels <- unique(data[[factor1]])
  results <- data.frame(Stage1 = character(), Stage2 = character(), p.value = numeric(), stringsAsFactors = FALSE)
  
  for (i in 1:(length(levels) - 1)) {
    for (j in (i + 1):length(levels)) {
      subset_data <- data %>% filter(data[[factor1]] %in% c(levels[i], levels[j]))
      contingency_table_pairwise <- table(subset_data[[factor1]], subset_data[[factor2]])
      fisher_result <- fisher.test(contingency_table_pairwise)
      results <- rbind(results, data.frame(Stage1 = levels[i], Stage2 = levels[j], p.value = fisher_result$p.value))
    }
  }
  return(results)
}

# Perform pairwise comparisons
pairwise_results <- pairwise_fisher(circ_data, "Stage", "ctDNA.MRD")
print(pairwise_results)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal", "Elevated"), labels = c("Normal", "Elevated"))
contingency_table <- table(circ_data$CA19.MRD, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - CA 19-9", 
       x = "CA 19-9", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Prognostic role of ctDNA at C5D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C5D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Multivariate regression model for DFS with ctDNA and CA 19-9
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
circ_datadf$Sex <- factor(circ_datadf$Sex, levels = c("Female", "Male"), labels = c("Female", "Male"))
circ_datadf$PrimSite <- factor(circ_datadf$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
circ_datadf$Chemo <- factor(circ_datadf$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis"))
circ_datadf$ResMarg <- factor(circ_datadf$ResMarg, levels = c("R0", "R1"))
circ_datadf$Stage <- factor(circ_datadf$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
circ_datadf$TP53 <- factor(circ_datadf$TP53, levels = c("WT", "Mut"))
circ_datadf$CA19.C5D1 <- factor(circ_datadf$CA19.C5D1, levels = c("Normal", "Elevated"))
circ_datadf$ctDNA.C5D1 <- factor(circ_datadf$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
surv_object<-Surv(time = circ_datadf$RFS.months, event = circ_datadf$RFS.Event) 
cox_fit <- coxph(surv_object ~ Sex + Age + PrimSite + Stage + Chemo + ResMarg + CA19.C5D1 + ctDNA.C5D1, data=circ_datadf) 
ggforest(cox_fit, data = circ_datadf, main = "Multivariate Regression Model for DFS - Landmark analysis", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```

#Univariate regression model for factors used at the C5D1 MVA
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Sex <- factor(circ_data$Sex, levels = c("Female", "Male"), labels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Sex, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
cox_fit <- coxph(surv_object ~ Age, data=circ_data) #univariate for age
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA")) #univariate for Primary Site
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ Stage, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=3, palette=c("blue","green","red"), title="DFS - ctDNA C5D1 - Stage", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("II", "III", "IV"), legend.title="")
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV")) #univariate for Stage
cox_fit <- coxph(surv_object ~ Stage, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis")) #univariate for Chemotherapy
cox_fit <- coxph(surv_object ~ Chemo, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1")) #univariate for Resection margin
cox_fit <- coxph(surv_object ~ ResMarg, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$CA19.C5D1 <- factor(circ_data$CA19.C5D1, levels = c("Normal", "Elevated")) #univariate for CA 19-9 C5D1
cox_fit <- coxph(surv_object ~ CA19.C5D1, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")) #univariate for ctDNA C5D1
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA at C5D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C5D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C5D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Prognostic role of ctDNA at C8D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C8D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA at C8D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C8D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C8D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Prognostic role of ctDNA anytime post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA anytime post-surgery", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA anytime post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA anytime post-surgery", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Prognostic role of ctDNA Dynamics
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 48] <- 48
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) 
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="ctDNA Dynamics | Pre-treatment - On-treatment", ylab= "Disease Free Survival", xlab="Time from Surgery (Months)", legend.title="") 
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)  
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#OS ctDNA Dynamics
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event) 
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("purple","green","blue","red"), title="MRD Dynamics | Pre-treatment - On-treatment", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.title="") 
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)  
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#OP for pts converted positive during ACT
```{r}
setwd("~/Downloads") 
clinstage<- read.csv("ASAN_Cholangio_OP.csv")
clinstage_df<- as.data.frame(clinstage)
clinstage_df <- clinstage_df[clinstage_df$ctDNA.Dynamics=="Converted Positive",]

##Overview plot - stratified by Stage
oplot_stratify <-swimmer_plot(df=clinstage_df,
                              id='PatientName',
                              end='fu.diff.months',
                              #name_fill='Arm',
                              col="gray",
                              alpha=0.75,
                              width=.01,
                              base_size = 14)
oplot_stratify <- oplot_stratify + theme(panel.border = element_blank())
oplot_stratify <- oplot_stratify + scale_y_continuous(breaks = seq(0, 108, by = 6))
oplot_stratify <- oplot_stratify + labs(x ="Patients" , y="Months from Surgery")
oplot_stratify

##plot events
oplot_ev3 <- oplot_stratify + swimmer_points(df_points=clinstage_df,
                                             id='PatientName',
                                             time='date.diff.months',
                                             name_shape ='Event_type',
                                             name_col = 'Event',
                                             size=3.5,fill='black',
                                             #col='darkgreen'
)
oplot_ev3

#Shape customization to Event_type

oplot_ev3.1 <- oplot_ev3 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4, 5, 23, 7, 15),breaks=c('ctDNA_neg', 'ctDNA_pos', 'Imaging', 'Surgery', 'Death', "cea_neg", "cea_pos", "ca19_neg", "ca19_pos"))

oplot_ev3.1

#plot treatment

oplot_ev4 <- oplot_ev3.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev4 <- oplot_ev4 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev4  


#colour customization
# orange=ACT, Black=Death, Red=PD, ctDNA negative=white, ctDNA positive=black, Surgery=blue, TURBT=gray 
oplot_ev4.2 <- oplot_ev4 + ggplot2::scale_color_manual(name="Event",values=c( "orange", "purple", "blue", "black", "black", "red", "blue", "blue"))
oplot_ev4.2
```


#ctDNA clearance proportions by chemotherapy regimen
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]

circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels = c("Converted Negative", "Persistently Positive"), labels = c("Clearance", "No Clearance"))
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"))
contingency_table <- table(circ_data$Chemo, circ_data$ctDNA.Dynamics)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance by Regimen", 
       x = "Regimen", 
       y = "Patients (%)", 
       fill = "ctDNA Clearance",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Clearance" = "blue", "No Clearance" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```


#Prognostic role of ctDNA C5D1 on Chemotherapy - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C5D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C5D1.Chemo = case_when(
    Chemo == "CAP" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
    Chemo == "CAP" & ctDNA.C5D1 == "POSITIVE" ~ 2,
    Chemo == "GemCis" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
    Chemo == "GemCis" & ctDNA.C5D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.Chemo, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1.Chemo) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C5D1.Chemo <- factor(circ_data$ctDNA.C5D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.Chemo, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#Prognostic role of ctDNA C8D1 on Chemotherapy - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C8D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C8D1.Chemo = case_when(
    Chemo == "CAP" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
    Chemo == "CAP" & ctDNA.C8D1 == "POSITIVE" ~ 2,
    Chemo == "GemCis" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
    Chemo == "GemCis" & ctDNA.C8D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.Chemo, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1.Chemo) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C8D1.Chemo <- factor(circ_data$ctDNA.C8D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.Chemo, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#Prognostic role of ctDNA C5D1 on Primary Site - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C5D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C5D1.PrimSite = case_when(
    PrimSite == "dCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
    PrimSite == "dCCA" & ctDNA.C5D1 == "POSITIVE" ~ 2,
    PrimSite == "pCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
    PrimSite == "pCCA" & ctDNA.C5D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.PrimSite, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1.PrimSite) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C5D1.PrimSite <- factor(circ_data$ctDNA.C5D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.PrimSite, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#Prognostic role of ctDNA C8D1 on Primary Site - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C8D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C8D1.PrimSite = case_when(
    PrimSite == "dCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
    PrimSite == "dCCA" & ctDNA.C8D1 == "POSITIVE" ~ 2,
    PrimSite == "pCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
    PrimSite == "pCCA" & ctDNA.C8D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.PrimSite, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1.PrimSite) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C8D1.PrimSite <- factor(circ_data$ctDNA.C8D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.PrimSite, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```

